title: Robust PCA based method for discovering differentially expressed genes.
abstract: How to identify a set of genes that are relevant to a key biological process is an important issue in current molecular biology. In this paper, we propose a novel method to discover differentially expressed genes based on robust principal component analysis (RPCA). In our method, we treat the differentially and non-differentially expressed genes as perturbation signals S and low-rank matrix A, respectively. Perturbation signals S can be recovered from the gene expression data by using RPCA. To discover the differentially expressed genes associated with special biological progresses or functions, the scheme is given as follows. Firstly, the matrix D of expression data is decomposed into two adding matrices A and S by using RPCA. Secondly, the differentially expressed genes are identified based on matrix S. Finally, the differentially expressed genes are evaluated by the tools based on Gene Ontology. A larger number of experiments on hypothetical and real gene expression data are also provided and the experimental results show that our method is efficient and effective.
authors.affiliation: Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China.
authors.forename: Jin-Xing
authors.initials: JX
authors.lastname: Liu
authors.forename: Yu-Tian
authors.initials: YT
authors.lastname: Wang
authors.forename: Chun-Hou
authors.initials: CH
authors.lastname: Zheng
authors.forename: Wen
authors.initials: W
authors.lastname: Sha
authors.forename: Jian-Xun
authors.initials: JX
authors.lastname: Mi
authors.forename: Yong
authors.initials: Y
authors.lastname: Xu
mesh_terms: D003110:Colonic Neoplasms / Q000235:genetics; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D025341:Principal Component Analysis / Q000379:methods*